similar to: minimal attributes to get se.fit

In summary.glm I'm trying to get a better feel for the z output. The following lines can be found in the function 1 if (p > 0) { 2 p1 <- 1:p 3 Qr <- object$qr 4 coef.p <- object$coefficients[Qr$pivot[p1]] 5 covmat.unscaled <- chol2inv(Qr$qr[p1, p1, drop = FALSE]) 6 dimnames(covmat.unscaled) <- list(names(coef.p), names(coef.p))

Win2k, R1.6.2. I've been using Splus 6.1 and wanted to try the same regression analysis in R. Using "names( blah.lm )" in R yields [1] "coefficients" "residuals" "effects" "rank" [5] "fitted.values" "assign" "qr" "df.residual" [9] "xlevels"

R developers, I have a first attempt to make an aov function. Eventually I want to build in Error() structure, but first I am trying to get this presentable for balanced data with only a single stratum, just using residual error. I am following R. M. Heiberger's Computation for the Analysis of Designed Experiments, Wiley (1989) I a using a wrapper (aov.bal) to call the

Hello. I started using the sem package in R and after a lot of searching and trying things I am still having difficulty. I get the following error message when I use the sem() function: Warning message: In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : Could not compute QR decomposition of Hessian. Optimization probably did not converge. I started with a

Hi, While exploring how summary.lm generated its output I came across a section that left me puzzled. at around line 57 R <- chol2inv(Qr$qr[p1, p1, drop = FALSE]) se <- sqrt(diag(R) * resvar) I'm hoping somebody could explain the logic of these to steps or alternatively point me in the direction of a text that will explain these steps. In particular I'm puzzled

Hi, I am running a simple linear model with (say) 5 independent variables. Is there a simple way of getting the variance-covariance matrix of the coeffcient estimates? None of the values of the lm() seem to provide this. Thanks in advance, Ritwik Sinha rsinha@darwin.cwru.edu Grad Student Case Western Reserve University [[alternative HTML version deleted]]

# r-bugs@r-project.org The problem occurs when the model matrix has a single column. > elastic <- data.frame(stretch=c(46,54,48,50,44,42,52), distance=c(183,217,189,208,178,150,249)) > elastic.lm <- lm(distance ~ -1 + stretch, data=elastic) > predict(elastic.lm,se=T) Error in XRinv^2 %*% rep(res.var, p) : non-conformable arguments The fix is to replace XRinv <-

Hi list, can I extract the cov.unscaled ("the unscaled covariance matrix") from a gls fit (package nlme), like with summary.lm? Background: In a fixed effect meta analysis regression the standard errors of the coefficients can be computed as sqrt(diag(cov.unscaled)) where cov.unscaled is (X'WX). I try do do this with a gls-fit. Thanks, Sven

Hi, I have a problem with fitting a model: I made a dataframe with this data: a <- 1:3 b <- 1:3 c <- c(3, 2, 3, 2, 1, 2, 3, 2, 3) df <- expand.grid(a,b) df$result <- c names(df) <- c("A","B", "result") Although I can make a graph of the data: require(lattice) wireframe(result~A*B, data=df) I can't get a model to

Dear friends, After fitting a generalized linear models ,i hope to get the variance of alpha,variance of beta and their covariance, that is , the variance-covariance matrix/information of alpha and beta , suppose *B* is the object of GLMs, i use attributes(B) to look for the options ,but can't find it, anybody knows how to get it? > attributes(B) $names [1] "coefficients"

Hello all. First of all, I have only been using are a short time and I'm not an expert in statistics either. I have the following problem. I'm working with measurements of physical samples, each measurement has about 4000 variables. I have 33 of those samples. From those 400 variables I deduced through non-statiscal means that I needed about 200 of them. I read those into a data.frame

Forgive me if I'm posting to the wrong place....It's my first time posting. Here's the situation: I'm using the sem package and making path diagrams using path.diagrams. Suppose I have the following code: #install.packages("ggm") require(ggm) cor = rcorr(7) nm = c("SOF", "IWF", "PWF", "FSC", "FSF", "EF",

Following p.206 of "Statistical Models in S", I wish to change the code for summary.glm() so that it estimates the dispersion for binomial & poisson models when the parameter dispersion is set to zero. The following changes [insertion of ||dispersion==0 at one point; and !is.null(dispersion) at another] will do the trick: "summary.glm" <- function(object, dispersion =

Dear R users, I'm trying to fit a set an ODE to an experimental time series. In the attachment you find the R code I wrote using modFit and modCost of FME package and the file of the time series. When I run summary(Fit) I obtain this error message, and the values of the parameters are equal to the initial guesses I gave to them. The problem is not due to the fact that I have only one

Dear list, I am looking for an implementation of random rotation matrix generation in R to do a rotation test: I want to use the matrices to create random multivariate normal matrices with common covariance structure and mean based on an observed data matrix. The rRotationMatrix-function in the mixAK-package is an option, but as far as I can tell I need to draw rotation matrices with determinant

Hi all, Regular and avid readers of this column will know that Don Driscoll and I have recently posted two messages requesting assistance concerning an apparent failure of "se.contrast" to produce an se for a contrast. So far, an ominous silence rings in our ears, but read on Gentle Reader, and see if even the machinations of "debug" doesn't stimulate you to respond with a

The man page for se.contrast, when describing the contrast.obj parameter, states that "Multiple contrasts should be specified by a matrix as returned by contrasts." When doing an unbalanced single factor ANOVA, using a contrast.obj as returned by contrasts results in the following error from qr.qty when se.contrast is called: Error in qr.qty(object$qr, contrast) : qr and y must have

Dear R-help, I would like to compute the variance for the proportion of treatment effect by a surrogate in a survival model (Lin, Fleming, and De Gruttola 1997 in Statistics in Medicine). The paper mentioned that the covariance matrix matches that of the covariance matrix estimator for the marginal hazard modelling of multiple events data (Wei, Lin, and Weissfeld 1989 JASA), and is implemented

I tried lrm in library(Design) but there is always some error message. Is this function really doing the weighted logistic regression as maximizing the following likelihood: \sum w_i*(y_i*\beta*x_i-log(1+exp(\beta*x_i))) Does anybody know a better way to fit this kind of model in R? FYI: one example of getting error message is like: > x=runif(10,0,3) > y=c(rep(0,5),rep(1,5)) >

Dear R-experts! I have y(x) data, dim(y) == dim(x) == c(2000, 2) I'd like to fit them with nls: fit.result <- nls ( y ~ f(x, p1, p2, p3), start = list(p1 = ... , p2 = .. , p3 = ..) ) Actually I want to fit y[,1] ~ x[,1] and y[,2] ~ x[,2] *simulaneously*, with the same parameters set {p1, p2, p3}. I tried to feed R tha above formula, R errors with: